Creating stimuli in R with ggplot - r

Question

I am trying to generate my own stimuli for an experiment using R. Below is the code that creates my (x,y) coordinates using the rnorm() with different a sample size of 100, different means and sd. I also create another variable to represent the size of the circles, which are determined by the runif().
dt <- data.frame(x = NA,
y = NA,
size = NA,
M = NA,
sd = NA,
col = NA,
iter = NA)
sa<-0
mySD<-c(5, 15)
myMeans<-c(35, 45)
colors<-c("Blues", "Reds")
for(i in 1:10){
for(s in mySD){
for(m in myMeans){
x = abs(rnorm(n=1, mean=m, sd=s))
y = abs(rnorm(n=1, mean=m, sd=s))
size = runif(1, 1, 25) #select a random x speed between [25,35]
sa<-sa+1
dt[sa,] <- NA
dt$x[sa]<-x
dt$y[sa]<-y
dt$M[sa]<-m
dt$sd[sa]<-s
dt$size[sa]<-size
dt$iter[sa]<-i
}
}
}
}
Next, I want to use ggplot(dt, aes(x, y, size=size) to plot. I want to randomly select 4 (x,y) values to plot for one graph, then 8 for another, then 16 for another, etc. Basically, I want to plot different graphs with a different number of data points. For example, some graphs that you would see would have 4 data points that vary by size and color, others would have 32 data points that vary in size and color. I m not sure how to select a set of unique data points from the data frame that I created. Any help would be great. I'm pretty new to R.

Answer 1

Here are two ways - depending if you wanted each group to not contain points from any other group.
I'll just use a dummy data frame that just has columns x, y, and size.
library(tidyverse)
dt <- tibble(x = runif(100), y = runif(100), size = runif(100))
Allowing groups to share the same points
Create a vector for the size of each group.
sample_sizes <- 2^(seq_len(4) + 1)
sample_sizes
#> [1] 4 8 16 32
Randomly sample the data frame and add a group column.
sampled <- map_dfr(
sample_sizes,
~sample_n(dt, .),
.id = "group"
)
Plot using facets.
ggplot(sampled, aes(x, y, size = size)) +
geom_point() +
facet_wrap(~group)
Requiring groups to have different points
First, we need a way to generate four 1s, eight 2s etc. This can be done using log2 and some tricks.
groups <- floor(log2(seq_len(nrow(dt)) + 3)) - 1
groups
#> [1] 1 1 1 1 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4
#> [36] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5
#> [71] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
Shuffle this vector and add it as a column.
dt$group <- sample(groups)
Facet using this new column to generate the desired plots.
ggplot(dt, aes(x, y, size = size)) +
geom_point() +
facet_wrap(~group)

Answer 2

First of all, the question's data creation code can be greatly simplified, rewritten with no loops at all. R is a vectorized language and the following will create a data frame with the same structure.
Don't forget to set the RNG seed, in order to make the results reproducible.
library(ggplot2)
set.seed(2020) # make the results reproducible
sd <- rep(rep(mySD, each = 2), 10)
M <- rep(myMeans, 2*10)
x <- abs(rnorm(n = 40, mean = M, sd = sd))
y <- abs(rnorm(n = 40, mean = M, sd = sd))
size <- runif(40, 1, 25)
iter <- seq_along(x)
dt2 <- data.frame(x, y, size, M, sd, iter)
dt2$col <- c("blue", "red")
Now the plots. The following function accepts a data frame X as its first argument and a number of points to draw as the second one. Then plots n points chosen at random with color col and size (a continuous variable) size.
plot_fun <- function(X, n){
Colors <- unique(X[["col"]])
Colors <- setNames(Colors, Colors)
i <- sample(nrow(X), n)
g <- ggplot(X[i,], aes(x, y, size = size, color = col)) +
geom_point() +
scale_color_manual(values = Colors) +
theme_bw()
g
}
plot_fun(dt2, 8)
To plot several values for n, produce the plots with lapply then use grid.arrange from package gridExtra.
plot_list <- lapply(c(4,8,16,32), function(n) plot_fun(dt2, n))
gridExtra::grid.arrange(grobs = plot_list)
Individual plots are still possible with
plot_list[[1]]
plot_list[[2]]
and so on.
Another way is to use faceting. Write another function, plot_fun_facets assigning the number of points to a new variable in the sample data frames, n, and use that variable as a faceting variable.
plot_fun_facets <- function(X, n){
Colors <- unique(X[["col"]])
Colors <- setNames(Colors, Colors)
X_list <- lapply(n, function(.n){
i <- sample(nrow(X), .n)
Y <- X[i,]
Y$n <- .n
Y
})
X <- do.call(rbind, X_list)
g <- ggplot(X, aes(x, y, size = size, color = col)) +
geom_point() +
scale_color_manual(values = Colors) +
facet_wrap(~ n) +
theme_bw()
g
}
plot_fun_facets(dt2, c(4,8,16,32))